How it works?
Left picture shows cyclist and pedestrian counters that are located in Oulu.
In middle picture API is queried about current counters and their locations.
Right picture shows us the location of the counter in Tableau.
It is no secret that citizens of Oulu are known to love cycling. This is a result of significantly good routes, proper maintenance, and smart city sensors to optimize upkeep of the routes. If you are interested about which routes of Oulu are most popular, or just want to find out where the upcoming cycling roads ("Baanas") are going to be built in, then this visualization is great for you!
This project was done for the course: Business Intelligence: Applications and Projects. The goal of this project was to provide dashboards with different type visualizations to help stakeholders utilize business intelligence solutions in taking care of these cyclist routes.
Technologies: BI software Tableau .
Query language used to extract the data was GraphQL .
Data: Open data from Oulun Liikenne's live API : https://wp.oulunliikenne.fi/avoin-data/ (In Finnish).
Link to visualization: https://public.tableau.com/views/CyclingdatainOulu/CountersandMainroutes?:language=en-US&:display_count=n&:origin=viz_share_link
Left picture shows cyclist and pedestrian counters that are located in Oulu.
In middle picture API is queried about current counters and their locations.
Right picture shows us the location of the counter in Tableau.
Next questions in the project were, that how we can utilize this data. To initiate thought process, the following DIDM-cycle was used. It contains five iterative steps: Framing the issue, data collection, data analysis, data interpretation, and decision-making and communication.
Goal of the first step: Ask the right questions.
How to provide information for cyclists, while helping city of Oulu to prioritize maintenance related challenges?
Goal of the second step: Collect the data.
How to provide information for cyclists, while helping city of Oulu to prioritize maintenance related challenges?
API allows extracting the data of counters' locations, number of cyclists or pedestrians daily in counter, ongoing traffic maintenance announcements, weather stations in different parts of Oulu
,
current condition of the roads (Winter)
Goal of the third step: Analyze the data.
Most of the data analysis was done in form of transforming and cleansing the data to make it compatible with Tableau.
Data from the traffic counters involved most data analysis from the data sources, as this was mostly numerical data with. For example, the user types of crossing citizens were either 1 (Pedestrian) or 2 (Cyclist).
Certain public routes were identified to be mostly used for commute (High amount of cyclist at morning and afternoon), while other roads were used for leisure purposes (High amount of pedestrians during afternoon-evening)
Goal of the fourth step: Make sense out of data.
Gathering data from the traffic counters sometimes included weird outliers or missing data. Why?
As an example, Raati's traffic counter was noticed to provide high number road users in late July. Its probable cause is that the counter is located near the entrance of Qstock festival.
Other example comes from commonly used counter near Patosilta, which did not provide any values during busiest summer months.
After investigating the missing values, article was found from local newspaper where was told that Patosilta was undergoing a renovation as can be seen from pictures. This affected that counter was unavailable during that time, resulting missing data from summer of 2021.
Goal of the fifth step: Utilize data in decision-making.
To help city of Oulu and its citizens, certain high-priority roads can be recognized (Roads with most users). These can be labeled as either cyclist favored or pedestrian favored roads.
Cyclist favored roads can be provided with already existing bicycle repair stations, while pedestrian favored roads can be made more comfortable with providing recreational solution, such as outdoor gyms, benches, and info screens for tourists
Open data from smart city sensors enable innovative solutions to battle challenges challenges provided annually. This project introduces some ways that data can be utilized in decision-making when challenges are related to maintenance of the roads, or their conditions.
However, this can be improved even further.
Data gotten from GraphQL queries can be scheduled and automatized with REST API & Postman.
Integration of REST API output directly to tableau can give real-time data to provide answers to some of the questions.
API itself provides more information that was not utilized in this demonstration, such as traffic announcements, and data from weather stations.
Oulun Liikenne includes motorized road data as well: Traffic camera photos, road condition data, fluency situations of the roads (Busy - non busy), and on-going road works.
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